Feature anomaly model

DESCRIPTION

@@ -24,13 +24,16 @@ Imports:
     methods,
     checkmate,
     utils,
-    stringi
+    stringi,
+    Rcpp,
+    parallel
 Suggests: 
     tinytest,
     covr,
     knitr,
-    rmarkdown,
-    arrow
+    arrow,
+    rmarkdown
+LinkingTo: Rcpp
 Collate: 
     'clean_ProteinProspector.R'
     'clean_Metamorpheus.R'
@@ -46,6 +49,7 @@ Collate:
     'clean_MaxQuant.R'
     'clean_DIAUmpire.R'
     'MSstatsConvert_core_functions.R'
+    'RcppExports.R'
     'converters_DIANNtoMSstatsFormat.R'
     'converters_DIAUmpiretoMSstatsFormat.R'
     'converters_FragPipetoMSstatsFormat.R'
@@ -60,10 +64,12 @@ Collate:
     'converters_SpectronauttoMSstatsFormat.R'
     'utils_MSstatsConvert.R'
     'utils_annotation.R'
+    'utils_anomaly_score.R'
     'utils_balanced_design.R'
     'utils_checks.R'
     'utils_classes.R'
     'utils_clean_features.R'
+    'utils_data_health.R'
     'utils_documentation.R'
     'utils_dt_operations.R'
     'utils_filtering.R'

NAMESPACE

@@ -2,9 +2,11 @@
 
 S3method(as.data.frame,MSstatsValidated)
 S3method(as.data.table,MSstatsValidated)
+export(CheckDataHealth)
 export(DIANNtoMSstatsFormat)
 export(DIAUmpiretoMSstatsFormat)
 export(FragPipetoMSstatsFormat)
+export(MSstatsAnomalyScores)
 export(MSstatsBalancedDesign)
 export(MSstatsClean)
 export(MSstatsImport)
@@ -25,7 +27,9 @@ export(getDataType)
 export(getInputFile)
 exportMethods(getDataType)
 exportMethods(getInputFile)
+import(Rcpp)
 import(data.table)
+import(parallel)
 importFrom(data.table,as.data.table)
 importFrom(data.table,fread)
 importFrom(data.table,melt)
@@ -37,3 +41,4 @@ importFrom(log4r,file_appender)
 importFrom(methods,new)
 importFrom(stats,na.omit)
 importFrom(utils,sessionInfo)
+useDynLib(MSstatsConvert, .registration = TRUE)

R/MSstatsConvert_core_functions.R

@@ -322,6 +322,7 @@ setMethod("MSstatsClean", signature = "MSstatsProteinProspectorFiles",
 #' names defined by the names of this list and values corresponding to its elements
 #' will be added to the output `data.frame`.
 #' @param aggregate_isotopic logical. If `TRUE`, isotopic peaks will by summed.
+#' @param anomaly_metrics character vector of names of columns with quality metrics. Default is missing and is not required if anomaly model not run.
 #' @param ... additional parameters to `data.table::fread`.
 #' 
 #' @return data.table
@@ -363,7 +364,7 @@ MSstatsPreprocess = function(
                             summarize_multiple_psms = max),
     score_filtering = list(), exact_filtering = list(), 
     pattern_filtering = list(), columns_to_fill = list(), 
-    aggregate_isotopic = FALSE, ...
+    aggregate_isotopic = FALSE, anomaly_metrics = c(), ...
 ) {
     .checkMSstatsParams(input, annotation, feature_columns,
                         remove_shared_peptides,
@@ -380,8 +381,10 @@ MSstatsPreprocess = function(
     input = .handleIsotopicPeaks(input, aggregate_isotopic)
     input = .filterFewMeasurements(input, 1, FALSE)
     input = .handleSharedPeptides(input, remove_shared_peptides)
-    input = .cleanByFeature(input, feature_columns, feature_cleaning)
-    input = .handleSingleFeaturePerProtein(input, remove_single_feature_proteins)
+    input = .cleanByFeature(input, feature_columns, 
+                            feature_cleaning, anomaly_metrics)
+    input = .handleSingleFeaturePerProtein(input, 
+                                           remove_single_feature_proteins)
     input = .mergeAnnotation(input, annotation)
     .fillValues(input, columns_to_fill)
     .adjustIntensities(input)
@@ -406,6 +409,7 @@ MSstatsPreprocess = function(
 #' If "na_to_zero", missing values will be replaced by zeros.
 #' @param remove_few lgl, if TRUE, features with one or two measurements 
 #' across runs will be removed.
+#' @param anomaly_metrics character vector of names of columns with quality metrics
 #' 
 #' @export
 #' @return data.frame of class `MSstatsValidated`
@@ -422,7 +426,7 @@ MSstatsPreprocess = function(
 #' 
 MSstatsBalancedDesign = function(input, feature_columns, fill_incomplete = TRUE,
                                  handle_fractions = TRUE, fix_missing = NULL,
-                                 remove_few = TRUE) {
+                                 remove_few = TRUE, anomaly_metrics = c()) {
     feature = NULL
 
     input[, feature := do.call(".combine", .SD), .SDcols = feature_columns]
@@ -435,7 +439,7 @@ MSstatsBalancedDesign = function(input, feature_columns, fill_incomplete = TRUE,
         getOption("MSstatsLog")("INFO", msg_fractions)
         getOption("MSstatsMsg")("INFO", msg_fractions)
     } 
-    input = .makeBalancedDesign(input, fill_incomplete)
+    input = .makeBalancedDesign(input, fill_incomplete, anomaly_metrics)
     msg_balanced = paste("** Updated quantification data to make balanced design.",
                          "Missing values are marked by NA")
     getOption("MSstatsLog")("INFO", msg_balanced)
@@ -445,7 +449,7 @@ MSstatsBalancedDesign = function(input, feature_columns, fill_incomplete = TRUE,
                   with = FALSE]
 
     getOption("MSstatsLog")("INFO", "\n")
-    .MSstatsFormat(input)
+    .MSstatsFormat(input, anomaly_metrics)
 }
 
 
@@ -512,3 +516,86 @@ MSstatsMakeAnnotation = function(input, annotation, ...) {
     getOption("MSstatsMsg")("INFO", msg)
     annotation
 }
+
+#' Run Anomaly Model
+#' 
+#' @param input data.table preprocessed by the MSstatsBalancedDesign function
+#' @param quality_metrics character vector of quality metrics to use in the model
+#' @param temporal_direction character vector of same length as quality_metrics indicating temporal feature to create.
+#' @param missing_run_count numeric, maximum allowed fraction of missing runs per feature.
+#' @param n_feat numeric, maximum number of features per protein to use in the model.
+#' @param run_order data.frame with two columns: Run and Order. Order should be numeric and indicate the order of runs.
+#' @param n_trees numeric, number of trees to use in the isolation forest model. Default is 100.
+#' @param max_depth numeric or "auto", maximum depth of each tree. Default is "auto" which sets depth to log2(N) where N is the number of runs.
+#' @param cores numeric, number of cores to use for parallel processing. Default is 1.
+#' @useDynLib MSstatsConvert, .registration = TRUE
+#' 
+#' @return data.table
+#' @export
+MSstatsAnomalyScores = function(input, quality_metrics, temporal_direction,
+                                missing_run_count, n_feat, run_order, n_trees, 
+                                max_depth, cores){
+
+    input = .prepareSpectronautAnomalyInput(input, quality_metrics, 
+                                            run_order, n_feat, 
+                                            missing_run_count)
+    input$PSM = paste0(input$PeptideSequence, input$PrecursorCharge)
+
+    for (i in seq_along(quality_metrics)){
+        if (temporal_direction[i] != FALSE){
+            quality_metrics = c(quality_metrics,
+                                paste0(quality_metrics[i], ".",
+                                       temporal_direction[i]))
+        }
+    }
+
+    input = .runAnomalyModel(input, 
+                             n_trees=n_trees, 
+                             max_depth=max_depth, 
+                             cores=cores,
+                             split_column="PSM",
+                             quality_metrics=quality_metrics)
+
+    subset_cols = c("Run", "ProteinName", "PeptideSequence", 
+                    "PrecursorCharge", "FragmentIon", 
+                    "ProductCharge", "IsotopeLabelType", 
+                    "Condition", "BioReplicate", 
+                    "Fraction", "Intensity", "AnomalyScores",
+                    quality_metrics)
+
+    subset_cols = subset_cols[subset_cols %in% names(input)]
+    input = input[, ..subset_cols]
+
+    return(input)
+
+}
+
+#' Takes as input the output of the SpectronauttoMSstatsFormat function and calculates various quality metrics to assess the health of the data. Requires Anomaly Detection model to be fit.
+#' 
+#' @param input MSstats input which is the output of Spectronaut converter
+#' @return list of two data.tables
+#' 
+#' @export
+CheckDataHealth = function(input){
+
+    input = as.data.table(input)
+
+    # All intensity characteristics
+    missing_percent = .checkMissing(input)
+    zero_truncated = .checkIntensityDistribution(input)
+
+    # Feature specific characteristics
+    input$Feature = paste(input$PeptideSequence,
+                          input$PrecursorCharge,
+                          input$FragmentIon,
+                          input$ProductCharge, sep="_")
+    feature_data = .checkFeatureSD(input)
+    outlier_info = .checkFeatureOutliers(input, feature_data)
+    feature_data = outlier_info[[1]]
+    outlier_summary = outlier_info[[2]]
+    feature_data = .checkFeatureCoverage(input, feature_data)
+
+    skew_results = .checkAnomalySkew(input)
+
+    return(list(feature_data, skew_results))
+}

R/RcppExports.R

@@ -0,0 +1,7 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+calculate_anomaly_score <- function(df, n_trees, max_depth) {
+    .Call(`_MSstatsConvert_calculate_anomaly_score`, df, n_trees, max_depth)
+}
+

R/clean_Spectronaut.R

@@ -1,32 +1,39 @@
 #' Clean raw Spectronaut output.
 #' @param msstats_object an object of class `MSstatsSpectronautFiles`.
 #' @param intensity chr, specifies which column will be used for Intensity.
+#' @param calculateAnomalyScores logical, whether to calculate anomaly scores
+#' @param anomalyModelFeatures character vector, specifies which columns will be used for anomaly detection model. Can be NULL if calculateAnomalyScores=FALSE.
 #' @return `data.table`
 #' @keywords internal
-.cleanRawSpectronaut = function(msstats_object, intensity) {
+.cleanRawSpectronaut = function(msstats_object, intensity,
+                                calculateAnomalyScores, 
+                                anomalyModelFeatures) {
   FFrgLossType = FExcludedFromQuantification = NULL
 
   spec_input = getInputFile(msstats_object, "input")
   .validateSpectronautInput(spec_input)
   spec_input = spec_input[FFrgLossType == "noloss", ]
-
-  if (is.character(spec_input$FExcludedFromQuantification)) {
-      spec_input = spec_input[FExcludedFromQuantification == "False", ]
-  } else {
-      spec_input = spec_input[!(as.logical(FExcludedFromQuantification)), ]
-  }
 
   f_charge_col = .findAvailable(c("FCharge", "FFrgZ"), colnames(spec_input))
   pg_qval_col = .findAvailable(c("PGQvalue"), colnames(spec_input))
+  interference_col = .findAvailable(c("FPossibleInterference"), 
+                                    colnames(spec_input))
+  exclude_col = .findAvailable(c("FExcludedFromQuantification"), 
+                               colnames(spec_input))
   cols = c("PGProteinGroups", "EGModifiedSequence", "FGCharge", "FFrgIon", 
            f_charge_col, "RFileName", "RCondition", "RReplicate", 
-           "EGQvalue", pg_qval_col, paste0("F", intensity))
+           "EGQvalue", pg_qval_col, interference_col, exclude_col,
+           paste0("F", intensity))
+  if (calculateAnomalyScores){
+    cols = c(cols, anomalyModelFeatures)
+  }
   cols = intersect(cols, colnames(spec_input))
   spec_input = spec_input[, cols, with = FALSE]
   data.table::setnames(
     spec_input, 
     c("PGProteinGroups", "EGModifiedSequence", "FGCharge", "FFrgIon",
-      f_charge_col, "RFileName", paste0("F", intensity), "RCondition", "RReplicate"),
+      f_charge_col, "RFileName", paste0("F", intensity), 
+      "RCondition", "RReplicate"),
     c("ProteinName", "PeptideSequence", "PrecursorCharge", "FragmentIon",
       "ProductCharge", "Run", "Intensity", "Condition", "BioReplicate"), 
     skip_absent = TRUE)